~~ Jo: alt text ends at metal category pie chart ~~ Cedar: figure out if document will knit ~~ Cedar: remove duplicate code chunks/rename code chunks
The Environmental Protection Agency’s database of Toxic Release Inventory lacks consistency in its observations and variables – though this is due to the unexplained environment that informs the data.
Every year, new chemicals to be reported to the EPA are added to the TRI’s list, which means the amount of chemical variables within this one decade of 2011-2021 goes from 513 reported chemicals to 545 reported chemicals which actually accounts for around 200 additions and many undocumented deletions of variables (not counting the 16 that were added to the dataset in 2011). Rules on what qualifies as the threshold for reporting also change as certain chemicals fall in and out of especially-close watch. Countrywide and statewide laws on environmental protection grant even more opportunity for data to drastically change year by year, though in technicality, the actual happenings at these facilities may not be changing much at all. Some of these laws – though not implying causation – will be clarified where relevant in the code.
Our goal is to use what data we find (that is reasonably accurate given the circumstances) in order to identify the layout of these waste chemicals across the United States. To reach this, we must ask some guiding questions: What are the relationships between differing industry sectors and their growing or declining locations? Are there any shifts in the levels of overall pollution during the past decade, and are these changes set in any specific part of the country? Finally, how has chemical use and waste (as well as waste procedures) changed in the past decade, and what do we know about these chemicals from the EPA to infer their threat to human health?
Our first question of locations deals with the trends of facilities’ presence, whether or not they are on tribal land, and their proximity to each other (possibly by industry sector) – which may help in assessing what is in the best interest of the people due to identifying the patterns of popularity at the same time as keeping safety and land protection a priority.
The five most common parent companies are as follows: U.S. Department of Defense, CRH Americas Inc, Cemex Inc, Argos U.S. Corp, and Berkshire Hathaway Inc. The first listed is a government agency responsible for U.S. military protection, followed by four different building/construction/transport materials companies.
## # A tibble: 7,925 × 3
## # Groups: standard_parent_co_name [5,483]
## standard_parent_co_name parent_co_name number_of_facilities
## <chr> <chr> <int>
## 1 <NA> <NA> 6433
## 2 US DEPARTMENT OF DEFENSE US DEPARTMENT OF DEFENSE 324
## 3 CRH AMERICAS INC CRH AMERICAS INC 252
## 4 CEMEX INC CEMEX INC 208
## 5 ARGOS USA CORP ARGOS USA CORP 186
## 6 BERKSHIRE HATHAWAY INC BERKSHIRE HATHAWAY INC 186
## 7 CLEAN HARBORS INC CLEAN HARBORS INC 136
## 8 KOCH INDUSTRIES INC KOCH INDUSTRIES INC 121
## 9 MARTIN MARIETTA MATERIALS INC MARTIN MARIETTA MATERIALS… 118
## 10 TYSON FOODS INC TYSON FOODS INC 110
## # ℹ 7,915 more rows
~~ add code for number of facilities per state
The large majority of facilities in this dataset are located in North America, but a significant number of U.S.-owned facilities are strewn across the global map in other territories – possibly shifting any inferences that can be made on the state of pollution solely in the U.S.
Though the percentage of facilities located on tribal land is less
than 1% (there are
sum(facility_location$tribal_yes_no == "YES") facilities on
tribal land), and we are curious about any differences in the amount of
chemicals used and released on tribal vs not tribal land.
## # A tibble: 2 × 2
## tribal_yes_no percent
## <chr> <dbl>
## 1 Not Tribal Land 99.7
## 2 Tribal Land 0.311
## # A tibble: 2 × 2
## # Groups: tribal_yes_no [2]
## tribal_yes_no n
## <chr> <int>
## 1 Not Tribal Land 30469
## 2 Tribal Land 95
Using these tables, we have set up a groundwork map for placing chemical usage, which leads us to ask: knowing the drastic difference in percent of facilities on tribal land or not, does the location of tribal land have any effect on the type and amount of chemical release when compared to facilities not on tribal land?
We can take points from history and U.S. law, knowing that tribes of the American West often do have significant health hazards due to the many power plants, factories, mines, waste dumps, and testing sites that are placed on their land – sometimes with permission as a means to acquiring much-needed funds, and sometimes out of a tribe’s power. This question will appear further in visualizations.
Within these observations of location, there is the question of each facility’s purpose. Condensing the purpose of a factory into just 11 categories, their individual patterns of location, chemical use, and pollution can be tracked. This prevalence allows us to ask: is there a correlation between an industry’s sector and their frequency of chemicals? Further, their frequency of specific types of chemicals – carcinogens, PFAS’, and PBT’?
The most common purpose of a U.S. facility is dealing with the production, waste, and wholesales of chemicals, accounting for almost 30% of U.S. industries as shown in the table. The least common accounting for about 0.4% is textile production which makes sense given trends in American imports.
## # A tibble: 11 × 2
## industry_sector_category percent
## <chr> <dbl>
## 1 Chemicals and Waste 29.2
## 2 Processed Materials 26.1
## 3 Petroleum 11.8
## 4 Machinery and Technology 10.5
## 5 Electric Utilities 5.81
## 6 Wood and Paper 5.71
## 7 Agriculture and Food Processing 4.81
## 8 Misc 2.79
## 9 <NA> 1.80
## 10 Mining 1.00
## 11 Textiles and Leather 0.400
Understandably fitting with the most common industry sector, the chemical-producing facilities (and processed materials) also use the most carcinogens (non-comprehensive), but the frequency seems to almost consistently grow with the prevalence of these sectors besides the food industry. That being said, every sector listed has a chemical usage that is majority non-carcinogenic.
Honing our observational questions down to just chemical trends of use and disposal now, we see how the laws of the EPA inform chemical procedures. Specifically in terms of the Clean Air Act, is there a differing correlation between the location and frequency of these specially-listed chemicals when compared to chemicals not listed on the Clean Air Act? And more broadly, is there any type, category, or closely-watched category of chemical that is most common despite the efforts of these laws?
~~ add table with number of chemicals tracked each year (maybe also graph)
The most commonly used chemical is lead, accounting for over 5% of use, followed by its compounds that make up almost 4% of use. Lead is known to cause poisoning of the human body, leading to neurological and reproductive health issues as well as impaired function of the kidneys and other body systems. Poisoning can be acquired through contaminated dust particles and products, contaminated drinking water, and contaminated soil.
## # A tibble: 627 × 2
## chemical percent
## <chr> <dbl>
## 1 Zinc compounds 0.00145
## 2 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) 0.00123
## 3 1,1,1,2-Tetrachloroethane 0.00123
## 4 1,1,1-Trichloroethane 0.00123
## 5 1,1,2,2-Tetrachloroethane 0.00123
## 6 1,1,2-Trichloroethane 0.00123
## 7 1,1-Dichloro-1-fluoroethane (HCFC-141b) 0.00123
## 8 1,1-Dimethylhydrazine 0.00123
## 9 1,2,3-Trichloropropane 0.00123
## 10 1,2,4-Trichlorobenzene 0.00123
## # ℹ 617 more rows
~~ why are these separate from the corresponding graphs????
The large majority of chemicals used are not elemental metals or metals in general. About 63% are identified in the Clean Air Act, leaving 37% not present. Over 28% of chemicals used in these facilities are known to be carcinogenic, 17% are PBTs, and 0.02% are PFAS’. These smaller percentages are still a concern, and are in-part new variables to track. PFAS did not have to be reported to the TRI until 2020 (making this decade of data insufficient), even though over 2,000 facilities were known to be producing them before the regulation. Therefore, it has far fewer observations in comparison to other chemicals though used since the 1940s and known to cause certain cancers, impairment of the immune system, reproductive issues, and abnormal developmental effects. The reporting threshold for that particular subset of chemicals is 100 pounds per year.
## # A tibble: 2 × 2
## elemental_metal_included percent
## <chr> <dbl>
## 1 NO 0.624
## 2 YES 0.00737
## # A tibble: 2 × 2
## pbt percent
## <chr> <dbl>
## 1 NO 82.8
## 2 YES 17.2
## # A tibble: 2 × 2
## pfas percent
## <chr> <dbl>
## 1 NO 100.
## 2 YES 0.0203
## # A tibble: 2 × 2
## clean_air_act_chemical percent
## <chr> <dbl>
## 1 NO 0.343
## 2 YES 0.288
## # A tibble: 2 × 2
## metal percent
## <chr> <dbl>
## 1 NO 0.587
## 2 YES 0.0438
## # A tibble: 6 × 2
## metal_category percent
## <chr> <dbl>
## 1 Non_Metal 0.528
## 2 Individually-listed compounds that contain metal 0.0434
## 3 Metal complound categories 0.0282
## 4 Elemental metals 0.0184
## 5 May contain metal 0.00848
## 6 Metals with qualifiers 0.00491
For above visualizations: elemental metals (some harmless, some causing neurotoxicity) keep a near-consistent distribution in the chemical data. The few increases this type of metal makes in the data is adjusted by the many additions of tracked chemicals in the dataframe, also causing the count for non-elemental-metals to increase. A similar state of increase goes for the amount of Clean Air Act chemicals and their growing amendments (still with more Clean Air Act chemicals than not) and with the general metals graph (more non-metals than metals, though both counts increasing). The amount of PBTs remains consistent with the non-PBTs increasing with new variables, and our two years of PFAS data show their consistency – though, of course, not much judgement can be made through this small piece of information, and the same must go for much of these environmentally-altered graphs.
For above visualizations: elemental metals (some harmless, some causing neurotoxicity) keep a near-consistent distribution in the chemical data. The few increases this type of metal makes in the data is adjusted by the many additions of tracked chemicals in the dataframe, also causing the count for non-elemental-metals to increase. A similar state of increase goes for the amount of Clean Air Act chemicals and their growing amendments (still with more Clean Air Act chemicals than not) and with the general metals graph (more non-metals than metals, though both counts increasing). The amount of PBTs remains consistent with the non-PBTs increasing with new variables, and our two years of PFAS data show their consistency – though, of course, not much judgement can be made through this small piece of information, and the same must go for much of these environmentally-altered graphs.
There are 1857 carcinogens being observed – a large number creating a small percentage.
## # A tibble: 1,857 × 2
## # Groups: chemical [187]
## chemical carcinogen
## <chr> <chr>
## 1 1,1,1,2-Tetrachloroethane YES
## 2 1,1,1,2-Tetrachloroethane YES
## 3 1,1,1,2-Tetrachloroethane YES
## 4 1,1,1,2-Tetrachloroethane YES
## 5 1,1,1,2-Tetrachloroethane YES
## 6 1,1,1,2-Tetrachloroethane YES
## 7 1,1,1,2-Tetrachloroethane YES
## 8 1,1,1,2-Tetrachloroethane YES
## 9 1,1,1,2-Tetrachloroethane YES
## 10 1,1,1,2-Tetrachloroethane YES
## # ℹ 1,847 more rows
There are 3797 non-carcinogens being observed.
## # A tibble: 3,797 × 2
## # Groups: chemical [440]
## chemical carcinogen
## <chr> <chr>
## 1 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 2 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 3 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 4 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 5 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 6 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 7 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 8 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 9 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 10 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## # ℹ 3,787 more rows
There are sum(chemical_info$pfas == "YES") observations
of PFAS chemicals in the dataset.
## # A tibble: 88 × 2
## # Groups: chemical [56]
## chemical pfas
## <chr> <chr>
## 1 1,1,2,2-Tetrahydroperfluorodecyl acrylate YES
## 2 1,1,2,2-Tetrahydroperfluorododecyl acrylate YES
## 3 1,1,2,2-Tetrahydroperfluorohexadecyl acrylate YES
## 4 1,1,2,2-Tetrahydroperfluorotetradecyl acrylate YES
## 5 1-Decanol, 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-heptadecafluoro- YES
## 6 1-Octanesulfonamide, 1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecafluoro… YES
## 7 1-Octanesulfonamide, 1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadecafluoro… YES
## 8 1-Octanesulfonamide, N-butyl-1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptade… YES
## 9 1-Propanaminium, 2-hydroxy-N,N,N-trimethyl-, 3-[(γ-ω-perfluoro-C6-20-a… YES
## 10 1-Propanaminium, 2-hydroxy-N,N,N-trimethyl-, 3-[(γ-ω-perfluoro-C6-20-a… YES
## # ℹ 78 more rows
There are sum(chemical_info$pfas == "NO") observations
of non-PFAS chemicals in the dataset.
## # A tibble: 5,566 × 2
## # Groups: chemical [571]
## chemical pfas
## <chr> <chr>
## 1 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 2 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 3 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 4 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 5 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 6 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 7 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 8 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 9 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 10 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## # ℹ 5,556 more rows
There are sum(chemical_info$pbt == "YES") observations
of PBT chemicals in the dataset.
## # A tibble: 233 × 2
## # Groups: chemical [23]
## chemical pbt
## <chr> <chr>
## 1 Aldrin YES
## 2 Aldrin YES
## 3 Aldrin YES
## 4 Aldrin YES
## 5 Aldrin YES
## 6 Aldrin YES
## 7 Aldrin YES
## 8 Aldrin YES
## 9 Aldrin YES
## 10 Aldrin YES
## # ℹ 223 more rows
There are sum(chemical_info$pbt == "NO") observations of
non-PBT chemicals in the dataset.
## # A tibble: 5,421 × 2
## # Groups: chemical [604]
## chemical pbt
## <chr> <chr>
## 1 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 2 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 3 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 4 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 5 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 6 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 7 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 8 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 9 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## 10 1,1,1,2-Tetrachloro-2-fluoroethane (HCFC-121a) NO
## # ℹ 5,411 more rows
From these distributions of observed chemicals, there is still the question of their disposal after use. With air, ground, and water being some possible points of disposal, we must assess the trends in these differing types of waste removal. In the past decade, what are these trends in pollution due to chemical release, whether on-site or off?
On-site release types: variables Underground,
Landfill, and Surface Impoundment are all
zero.
Pollution variable Releases is all zeros.
## # A tibble: 11 × 2
## chemical n
## <chr> <int>
## 1 Lead 47031
## 2 Nickel 28432
## 3 Chromium 27333
## 4 Ammonia 25778
## 5 Styrene 13327
## 6 Benzo[g,h,i]perylene 13248
## 7 Formaldehyde 8093
## 8 Mercury 4418
## 9 Chloroform 971
## 10 Arsenic 616
## 11 Phosphorus (yellow or white) 432
## # A tibble: 11 × 6
## # Groups: chemical [11]
## chemical pfas pbt clean_air_act_chemical carcinogen n
## <chr> <chr> <chr> <chr> <chr> <int>
## 1 Lead NO YES NO YES 47031
## 2 Nickel NO NO YES YES 28432
## 3 Chromium NO NO YES NO 27333
## 4 Ammonia NO NO NO NO 25778
## 5 Styrene NO NO YES YES 13327
## 6 Benzo[g,h,i]perylene NO YES YES NO 13248
## 7 Formaldehyde NO NO YES YES 8093
## 8 Mercury NO YES YES NO 4418
## 9 Chloroform NO NO YES YES 971
## 10 Arsenic NO NO YES YES 616
## 11 Phosphorus (yellow or wh… NO NO YES NO 432
## # A tibble: 14,994 × 4
## # Groups: standard_parent_co_name, parent_co_name [4,654]
## standard_parent_co_name parent_co_name facility_name number_of_chemicals
## <chr> <chr> <chr> <int>
## 1 CLEAN HARBORS INC CLEAN HARBORS INC CLEAN HARBOR… 11
## 2 CLEAN HARBORS INC CLEAN HARBORS INC CLEAN HARBOR… 11
## 3 DOW INC DOW INC THE DOW CHEM… 11
## 4 CLEAN HARBORS INC CLEAN HARBORS INC CLEAN HARBOR… 10
## 5 CLEAN HARBORS INC CLEAN HARBORS INC CLEAN HARBOR… 10
## 6 WASTE MANAGEMENT INC WASTE MANAGEMENT I… CHEMICAL WAS… 10
## 7 OLIN CORP OLIN CORP BLUE CUBE OP… 9
## 8 <NA> CENOVUS ENERGY INC. LIMA REFININ… 9
## 9 <NA> REPUBLIC SERVICES … US ECOLOGY N… 9
## 10 <NA> TRADEBE GP TRADEBE TREA… 9
## # ℹ 14,984 more rows
## # A tibble: 21 × 3
## chemical tribal_yes_no n
## <chr> <chr> <int>
## 1 Ammonia Not Tribal Land 25706
## 2 Ammonia Tribal Land 72
## 3 Arsenic Not Tribal Land 611
## 4 Arsenic Tribal Land 5
## 5 Benzo[g,h,i]perylene Not Tribal Land 13170
## 6 Benzo[g,h,i]perylene Tribal Land 78
## 7 Chloroform Not Tribal Land 960
## 8 Chloroform Tribal Land 11
## 9 Chromium Not Tribal Land 27202
## 10 Chromium Tribal Land 131
## # ℹ 11 more rows
## # A tibble: 56 × 2
## state chemical_use
## <chr> <int>
## 1 AL 11
## 2 AR 11
## 3 AZ 11
## 4 CA 11
## 5 CO 11
## 6 FL 11
## 7 GA 11
## 8 IA 11
## 9 IL 11
## 10 IN 11
## # ℹ 46 more rows
Note: Phosphorus is not faceted by industry sector because there is only one industry that uses it.
chemical_analysis_data %>%
ggplot(aes(x = chemical, y = on_site_total, fill = chemical)) +
geom_col(position = "dodge") +
labs(title = "Total On Site By Chemical",
subtitle = "On Site Release, Storage, and Disposal",
x = "Chemical",
y = "On Site Total",
fill = "Chemical") +
theme_minimal() +
coord_flip() +
scale_fill_viridis_d() +
facet_wrap(~ year)
chemical_analysis_data %>%
ggplot(aes(x = chemical, y = off_site_total, fill = chemical)) +
geom_col(position = "dodge") +
labs(title = "Total Off Site By Chemical",
subtitle = "Off Site Release, Storage, Treatment, and Disposal",
x = "Chemical",
y = "Off Site Total",
fill = "Chemical") +
theme_minimal() +
coord_flip() +
scale_fill_viridis_d() +
facet_wrap(~ year)
chemical_analysis_data %>%
filter(chemical == "Arsenic") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Arsenic Off Site Total",
subtitle = "Arsenic Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Styrene") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Styrene Off Site Total",
subtitle = "Styrene Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Formaldehyde") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Formaldehyde Off Site Total",
subtitle = "Formaldehyde Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Nickel") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Nickel Off Site Total",
subtitle = "Nickel Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Chromium") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Chromium Off Site Total",
subtitle = "Chromium Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Chromium") %>%
ggplot(aes(x = year, y = on_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Chromium On Site Total",
subtitle = "Chromium On Site Release, Storage, and Disposal",
x = "Year",
y = "On Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Lead") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Lead Off Site Total",
subtitle = "Lead Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Mercury") %>%
ggplot(aes(x = year, y = on_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Mercury On Site Total",
subtitle = "Mercury On Site Release, Storage, and Disposal",
x = "Year",
y = "On Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Ammonia") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Ammonia Off Site Total",
subtitle = "Ammonia Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Phosphorus") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Phosphorus Off Site Total",
subtitle = "Phosphorus Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d()
chemical_analysis_data %>%
filter(chemical == "Chloroform") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Chloroform Off Site Total",
subtitle = "Chloroform Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
chemical_analysis_data %>%
filter(chemical == "Benzo[g,h,i]perylene") %>%
ggplot(aes(x = year, y = off_site_total, fill = industry_sector_category)) +
geom_col(position = "dodge", just = 0.5) +
labs(title = "Benzo[g,h,i]perylene Off Site Total",
subtitle = "Benzo[g,h,i]perylene Off Site Release, Storage, Treatment, and Disposal",
x = "Year",
y = "Off Site Total",
fill = "Industry Sector Category") +
theme_minimal() +
scale_fill_viridis_d() +
facet_wrap(~ industry_sector_category)
In recognizing the increase of observations, increase in pollution trends, and the environment of increasingly strict reporting laws, we must be careful not to make the assumption that pollution is truly on the rise, and that conditions are getting worse in general. What the legal environment shows is an increase in control and recognition of needed chemical regulations. What the work shows is a very actively polluted environment irregardless of these laws, and the continued prevalence of chemicals that are known to have been used as weapons of war and for body-preservation and alteration – clearly harmful to human health. This is not to say that there is no improvement, but that there surely is much more that must be done.